Natural Language Processing for Computer Scientists and Data Scientists at a Large State University

Casey Kennington


Abstract
The field of Natural Language Processing (NLP) changes rapidly, requiring course offerings to adjust with those changes, and NLP is not just for computer scientists; it’s a field that should be accessible to anyone who has a sufficient background. In this paper, I explain how students with Computer Science and Data Science backgrounds can be well-prepared for an upper-division NLP course at a large state university. The course covers probability and information theory, elementary linguistics, machine and deep learning, with an attempt to balance theoretical ideas and concepts with practical applications. I explain the course objectives, topics and assignments, reflect on adjustments to the course over the last four years, as well as feedback from students.
Anthology ID:
2021.teachingnlp-1.21
Volume:
Proceedings of the Fifth Workshop on Teaching NLP
Month:
June
Year:
2021
Address:
Online
Editors:
David Jurgens, Varada Kolhatkar, Lucy Li, Margot Mieskes, Ted Pedersen
Venue:
TeachingNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
115–124
Language:
URL:
https://aclanthology.org/2021.teachingnlp-1.21
DOI:
10.18653/v1/2021.teachingnlp-1.21
Bibkey:
Cite (ACL):
Casey Kennington. 2021. Natural Language Processing for Computer Scientists and Data Scientists at a Large State University. In Proceedings of the Fifth Workshop on Teaching NLP, pages 115–124, Online. Association for Computational Linguistics.
Cite (Informal):
Natural Language Processing for Computer Scientists and Data Scientists at a Large State University (Kennington, TeachingNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.teachingnlp-1.21.pdf